





from __future__ import annotations

lowerCAmelCase_        =							[-1_0, -5, 0, 5, 5.1, 1_1, 1_3, 2_1, 3, 4, -2_1, -1_0, -5, -1, 0]
lowerCAmelCase_        =							[-5, 0, 5, 5.1, 1_1, 1_3, 2_1, -1, 4, -1, -1_0, -5, -1, 0, -1]






def 	lowerCamelCase_		(							_UpperCamelCase					)						->		list[float]:






				"""simple docstring"""


				snake_case_		:       List[Any]           =    []
				snake_case_		:       Any           =    len(_UpperCamelCase					)

				for i in range(_UpperCamelCase					):
								snake_case_		:       float           =    -1
								for j in range(i + 1					,							_UpperCamelCase					):
												if arr[i] < arr[j]:
																snake_case_		:       List[Any]           =    arr[j]
																break
								result.append(_UpperCamelCase					)
				return result






def 	lowerCamelCase_		(							_UpperCamelCase					)						->		list[float]:






				"""simple docstring"""


				snake_case_		:       List[str]           =    []
				for i, outer in enumerate(_UpperCamelCase					):
								snake_case_		:       float           =    -1
								for inner in arr[i + 1 :]:
												if outer < inner:
																snake_case_		:       int           =    inner
																break
								result.append(_UpperCamelCase					)
				return result






def 	lowerCamelCase_		(							_UpperCamelCase					)						->		list[float]:






				"""simple docstring"""


				snake_case_		:       Tuple           =    len(_UpperCamelCase					)
				snake_case_		:       list[float]           =    []
				snake_case_		:       list[float]           =    [-1] * arr_size

				for index in reversed(range(_UpperCamelCase					)					):
								if stack:
												while stack[-1] <= arr[index]:
																stack.pop()
																if not stack:
																				break
								if stack:
												snake_case_		:       Optional[Any]           =    stack[-1]
								stack.append(arr[index]					)
				return result


if __name__ == "__main__":
	from doctest import testmod
	from timeit import timeit

	testmod()
	print(next_greatest_element_slow(arr))
	print(next_greatest_element_fast(arr))
	print(next_greatest_element(arr))

	lowerCAmelCase_        =							(
	    '''from __main__ import arr, next_greatest_element_slow, '''
	    '''next_greatest_element_fast, next_greatest_element'''
	)
	print(
	    '''next_greatest_element_slow():''',
	    timeit('''next_greatest_element_slow(arr)''', setup=setup),
	)
	print(
	    '''next_greatest_element_fast():''',
	    timeit('''next_greatest_element_fast(arr)''', setup=setup),
	)
	print(
	    '''     next_greatest_element():''',
	    timeit('''next_greatest_element(arr)''', setup=setup),
	)





'''simple docstring'''



import unittest

from transformers import is_tf_available
from transformers.testing_utils import require_tf


if is_tf_available():
							import tensorflow as tf
							from tensorflow.python.eager import context
							from tensorflow.python.framework import ops

							from transformers import GradientAccumulator, create_optimizer
@require_tf
class 						lowerCamelCase_				(       unittest.TestCase      ):







						def        __lowercase     (	self :   List[Any]					,     lowerCAmelCase__ :   str					,     lowerCAmelCase__ :   Dict					,     lowerCAmelCase__ :   int					):

													"""simple docstring"""
													self.assertEqual(len(lowerCAmelCase__					)					,     len(lowerCAmelCase__					)					)
													for a, b in zip(lowerCAmelCase__					,     lowerCAmelCase__					):
																				self.assertAlmostEqual(lowerCAmelCase__					,     lowerCAmelCase__					,     delta=lowerCAmelCase__					)







						def        __lowercase     (	self :   Dict					):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE       :   List[Any]         =      GradientAccumulator()
													accumulator([tf.constant([1.0, 2.0]					)]					)
													accumulator([tf.constant([-2.0, 1.0]					)]					)
													accumulator([tf.constant([-1.0, 2.0]					)]					)
													with self.assertRaises(lowerCAmelCase__					):
																				accumulator([tf.constant([1.0, 1.0]					), tf.constant([2.0, 2.0]					)]					)
													self.assertEqual(accumulator.step					,     3					)
													self.assertEqual(len(accumulator.gradients					)					,     1					)
													self.assertListAlmostEqual(accumulator.gradients[0].numpy().tolist()					,     [-2.0, 5.0]					,     tol=1e-2					)
													accumulator.reset()
													self.assertEqual(accumulator.step					,     0					)
													self.assertListAlmostEqual(accumulator.gradients[0].numpy().tolist()					,     [0.0, 0.0]					,     tol=1e-2					)







						def        __lowercase     (	self :   List[str]					):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE       :   Optional[int]         =      None
													ops.enable_eager_execution_internal()
													SCREAMING_SNAKE_CASE       :   List[Any]         =      tf.config.list_physical_devices('''CPU'''					)
													if len(lowerCAmelCase__					) == 1:
																				tf.config.set_logical_device_configuration(
																				    physical_devices[0]					,     [tf.config.LogicalDeviceConfiguration(), tf.config.LogicalDeviceConfiguration()]					)
													SCREAMING_SNAKE_CASE       :   Tuple         =      tf.config.list_logical_devices(device_type='''CPU'''					)
													SCREAMING_SNAKE_CASE       :   List[Any]         =      tf.distribute.MirroredStrategy(devices=devices[:2]					)

													with strategy.scope():
																				SCREAMING_SNAKE_CASE       :   Optional[Any]         =      GradientAccumulator()
																				SCREAMING_SNAKE_CASE       :   Dict         =      tf.Variable([4.0, 3.0]					)
																				SCREAMING_SNAKE_CASE       ,SCREAMING_SNAKE_CASE       :   Optional[int]         =      create_optimizer(5e-5					,     10					,     5					)
																				SCREAMING_SNAKE_CASE       :   List[Any]         =      tf.Variable([0.0, 0.0]					,     trainable=lowerCAmelCase__					)

													def accumulate_on_replica(lowerCAmelCase__ :   List[str]					):
																				accumulator([gradient]					)

													def apply_on_replica():
																				optimizer.apply_gradients(list(zip(accumulator.gradients					,     [variable]					)					)					)

													@tf.function
													def accumulate(lowerCAmelCase__ :   str					,     lowerCAmelCase__ :   List[Any]					):
																				with strategy.scope():
																											SCREAMING_SNAKE_CASE       :   Optional[Any]         =      strategy.experimental_local_results(lowerCAmelCase__					)
																											local_variables[0].assign(lowerCAmelCase__					)
																											local_variables[1].assign(lowerCAmelCase__					)
																											strategy.run(lowerCAmelCase__					,     args=(gradient_placeholder,)					)

													@tf.function
													def apply_grad():
																				with strategy.scope():
																											strategy.run(lowerCAmelCase__					)

													def _check_local_values(lowerCAmelCase__ :   Dict					,     lowerCAmelCase__ :   int					):
																				SCREAMING_SNAKE_CASE       :   Tuple         =      strategy.experimental_local_results(accumulator._gradients[0]					)
																				self.assertListAlmostEqual(values[0].value()					,     lowerCAmelCase__					,     tol=1e-2					)
																				self.assertListAlmostEqual(values[1].value()					,     lowerCAmelCase__					,     tol=1e-2					)

													accumulate([1.0, 2.0]					,     [-1.0, 1.0]					)
													accumulate([3.0, -1.0]					,     [-1.0, -1.0]					)
													accumulate([-2.0, 2.0]					,     [3.0, -2.0]					)
													self.assertEqual(accumulator.step					,     3					)
													_check_local_values([2.0, 3.0]					,     [1.0, -2.0]					)
													apply_grad()
													self.assertListAlmostEqual(variable.value()					,     [4.0, 3.0]					,     tol=1e-2					)
													accumulator.reset()
													self.assertEqual(accumulator.step					,     0					)
													_check_local_values([0.0, 0.0]					,     [0.0, 0.0]					)




